Summary of Do Transformer World Models Give Better Policy Gradients?, by Michel Ma et al.
Do Transformer World Models Give Better Policy Gradients?
by Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D’Oro, Pierre-Luc Bacon
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach for reinforcement learning involves predicting future rewards by unrolling a neural network world model and backpropagating through the resulting computational graph to learn a policy. However, this method often becomes impractical for long horizons due to typical world models inducing hard-to-optimize loss landscapes. The paper explores whether transformers can efficiently propagate gradients over long horizons, finding that commonly-used transformer world models produce circuitous gradient paths that can be detrimental to long-range policy gradients. To address this challenge, the authors propose Actions World Models (AWMs), designed to provide more direct routes for gradient propagation. AWMs are integrated into a policy gradient framework that underscores the relationship between network architectures and policy gradient updates. The paper demonstrates that AWMs can generate optimization landscapes that are easier to navigate even when compared to those from the simulator itself, allowing transformer AWMs to produce better policies than competitive baselines in realistic long-horizon tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research suggests a new approach for reinforcement learning that uses transformers to predict future rewards. Instead of using traditional methods that can be tricky to optimize, this method uses neural networks to create a “world model” and then uses the transformer to learn a policy. The authors found that some previous approaches actually made things harder by creating circuitous paths for gradient propagation. To fix this, they created new models called Actions World Models (AWMs) that make it easier to optimize and learn better policies. |
Keywords
* Artificial intelligence * Neural network * Optimization * Reinforcement learning * Transformer
